The field of the invention is image analysis and processing. More particularly, the invention relates to systems and methods for processing and segmenting images, for example, for use in therapy planning, such as for radiation therapy.
Many modern medical treatments rely heavily on planning procedures that utilize medical images to facilitate the planning. For example, modern radiation therapy procedures are preceded by substantial planning processes that dictate how the radiation therapy will be performed. As another example, robotically-driven surgical procedures, include radiosurgery procedures, rely on extensive planning. Not surprisingly, the effectiveness of any particular planning procedure is necessarily limited by the detail and accuracy of the underlying information used for planning.
In treatment planning procedures, medical images provide the fundamental basis of information upon which planning is conducted. Traditionally, many planning procedures have relied upon anatomical images, such as are readily provided by computed tomography (CT) or magnetic resonance (MR) imaging systems. These and other modalities provide exceptional anatomical information, but often struggle to impart any physiological information.
More recently, imaging modalities, such as positron emission tomography (PET), have become a prevalent resource in medical planning because PET images (and other such imaging modalities, such as single photon emission tomography (SPECT) and the like) provide extensive physiological and/or biological information that is unavailable from imaging modalities such as CT and MR systems. For example, PET, in particular, has become a common tool used for radiotherapy target definition and for treatment assessment. PET images provide functional information that can be incorporated into the localization and planning process to further improve tumor delineation, especially when tumors are difficult to define from anatomical images, such as provided by CT systems, or when the tumor boundaries are not easily distinguished from the normal surrounding tissue. Another reason for integrating PET in the gross tumor volume (GTV) definition is its higher sensitivity and specificity for malignant disease. Therefore, a reliable and robust segmentation method is of utmost importance, given that under-dosing a tumor may lead to recurrence while over-dosing of the normal surrounding tissue could lead to severe side effects to the patients. Unfortunately, PET images provide poor anatomical detail and, as a result of this limitation and others, segmentation can be difficult.
Multiple approaches for segmentation have been proposed in the literature, but the most prevalent approach for auto-segmentation of PET target volumes is threshold segmentation, based on fixed uptake values or local contrast. More sophisticated auto-segmentation approaches include gradient-based, region-growing, texture-feature, and statistical-based segmentation methods. All of these methods have additional drawbacks associated with necessary parameter determination during the pre-processing steps. For example, in the case of the gradient-based approach, a pre-processing step using a bilateral filter is required to smooth the image. In the case of feature-based methods, a training data set is required and serves as a limiting factor in the accuracy. In the statistical approach, initial class estimates are required. The need of pre-processing steps or additional information make the use of these algorithms more complicated and the outcome dependent on choices made by the user. Particularly in light of the requirement of user input, these methods can have substantial variability.
In addition, PET images are affected by inherent uncertainties, such as physical, biological, and technical factors. The physical factors include acquisition mode and image reconstruction parameters. Biological factors refer to glucose level in the blood, uptake periods, and motion, among others. The technical factors include, but are not limited to, residual activity in the syringe, injection and calibration time, and incorrect synchronization of clocks of PET/CT and dose calibrator.
Therefore, it would be desirable to have a system and method for therapy planning, such as radiation therapy treatment planning, that benefits from the important information that can be derived from medical images including functional information, such as PET images and the like, but does not suffer from the limitations and uncertainties inherent in traditional planning methods, such as uncontrolled variability, such as created by user input or user variations.